Pragmatic Trial of Remote tDCS and Somatosensory Training for Phantom Limb Pain with Machine Learning to Predict Treatment Response

利用机器学习预测治疗反应的远程 tDCS 和体感训练治疗幻肢痛的实用试验

• 批准号: 10434306
• 负责人: Felipe Fregni
• 金额: $ 61.21万
• 依托单位: SPAULDING REHABILITATION HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10434306
• 关键词: Address; Affect; Anodes; Anterior; Area; Behavior Therapy; Behavioral; Biological Markers; Brain; Characteristics; Chronic; Clinical; Combined Modality Therapy; Conduct Clinical Trials; Control Groups; Data; Devices; Disease; Effectiveness; Esthesia; Force of Gravity; Goals; Hand; Health Professional; Home; Individual; Intervention; Investigation; Knowledge; Life; Machine Learning; Meta-Analysis; Modernization; Motor; Motor Cortex; Movement; Occupational Therapy; Operative Surgical Procedures; Pain; Pain management; Paper; Patients; Phantom Limb; Phantom Limb Pain; Pharmacological Treatment; Pharmacology; Phenotype; Physical therapy; Population Decreases; Prediction of Response to Therapy; Protocols documentation; Publishing; Randomized; Rehabilitation therapy; Research; Resistance; Surrogate Markers; Syndrome; Techniques; Testing; Therapeutic; Training; Underrepresented Populations; Validation; Visit; base; chronic neuropathic pain; chronic pain; clinical predictors; cognitive training; cost; design; effectiveness evaluation; effectiveness testing; efficacy testing; evidence base; experience; functional restoration; gray matter; heart rate variability; improved; indexing; innovation; machine learning algorithm; machine learning method; neural circuit; neuroimaging; neurological rehabilitation; neuronal circuitry; neurophysiology; neuroregulation; noninvasive brain stimulation; novel; pain patient; pain reduction; pain relief; painful neuropathy; portability; pragmatic trial; predicting response; predictive test; relating to nervous system; remote assessment; remote therapy; research study; residual limb; response; response biomarker; somatosensory; spinal cord injury pain; statistical and machine learning; treatment as usual; treatment response; treatment strategy; trial design

PROJECT SUMMARY/ABSTRACT: Phantom limb pain (PLP) is considered an extremely hard-to-treat disorder, given that traditional treatments are not effective in targeting the maladaptive neuronal circuits associated with chronic pain. Transcranial direct current stimulation (tDCS) is a non-invasive, safe brain stimulation technique that has been shown to revert maladaptive plasticity as well as reduce pain in neuropathic pain and other pain syndromes. Our previous R01 on this topic has demonstrated the efficacy of tDCS combined with somatosensory training in a controlled setting to improve pain and that this intervention changes PLP associated cortical plasticity. Our previous R01 also shown pain phenotypes based on PLP characteristics that are more responsive to this treatment. The objective of this renewal is to provide novel data to address critical knowledge gaps such as (i) testing a portable device that would reach underrepresented populations; (ii) validation of this therapy in a more pragmatic setting; (iii) confirmation and testing of predictors of response with statistical and machine learning techniques; and (iv) testing the parasympathetic tone changes (with the remote assessment) as a biomarker of neuropathic pain relief. The central hypothesis is that a combination of home-based tDCS and somatosensory therapy will reduce pain in PLP patients. Our long-term goal is to develop a cheap, efficacious, safe, and practical treatment for PLP. Our rationale is that understanding the effects of tDCS in a real-life setting will validate this treatment for PLP and identify predictors of response to this treatment will help health professionals better target and more precisely treat individuals with this condition. Our specific aims will test the following hypotheses: (Aim 1) tDCS combined with somatosensory therapy will be associated with a significantly larger effect size (of at least 1) compared to the control condition in pain reduction; (Aim 2) identifying predictors of response of this combined treatment using machine learning algorithms will help identify different pain phenotypes in patients with PLP and improve their target treatment; (Aim 3) combined treatment will bolster the parasympathetic tone (as indexed by higher heart rate variability) and reduce sympathetic activation, changes which will be correlated with PLP decreases. This contribution is significant because, although several studies have tested the efficacy of tDCS for chronic pain, there is a need to evaluate its effectiveness in a real-world setting, and this proposal provides critical data to develop a safe and unique intervention to be applied at home, which can therefore increase its access to underrepresented populations and decrease therapeutic costs. This investigation will also provide mechanistic data on predictors of response and changes in parasympathetic tone associated with this intervention. The proposed research is innovative because it offers a pragmatic trial design for PLP treatment and aims to validate a home-based tDCS device that is feasible and able to provide longer treatments remotely. This proposal is also investigating HRV as a surrogate marker for reducing PLP and assessing its feasibility in a real-life setting. Finally, this proposal validates predictors of response to PLP treatment using machine learning algorithms.

项目总结/摘要： 幻肢痛（PLP）被认为是一种非常难以治疗的疾病，因为传统的治疗方法 不能有效地靶向与慢性疼痛相关的适应不良的神经元回路。经颅直流 电流刺激（tDCS）是一种无创、安全的脑刺激技术，已被证明可以恢复 适应不良的可塑性以及减少神经性疼痛和其他疼痛综合征的疼痛。我们以前的R 01 关于这个主题的研究已经证明了tDCS结合体感训练在受控环境中的有效性 改善疼痛，并且这种干预改变了PLP相关的皮质可塑性。我们之前的R 01也 显示基于PLP特征的疼痛表型对这种治疗更敏感。客观 此次更新的目的是提供新的数据，以解决关键的知识差距，如（i）测试便携式设备 ㈡在更务实的环境中验证这种疗法; ㈢ 利用统计和机器学习技术确认和测试响应预测因素;以及（iv） 测试副交感神经张力变化（用远程评估）作为神经性疼痛的生物标志物 救灾中心假设是，以家庭为基础的tDCS和体感疗法的组合将减少 PLP患者的疼痛。我们的长期目标是开发一种便宜，有效，安全和实用的PLP治疗方法。 我们的理由是，了解tDCS在现实生活中的作用将验证这种治疗PLP 并确定对这种治疗反应的预测因素将有助于卫生专业人员更好地瞄准， 治疗患有这种疾病的人。我们的具体目标将测试以下假设：（目标1）tDCS结合 与躯体感觉疗法相比， 减轻疼痛的对照条件;（目的2）确定这种联合治疗反应的预测因素 使用机器学习算法将有助于识别PLP患者的不同疼痛表型， （目标3）联合治疗将加强副交感神经张力（如由更高的 心率变异性）和减少交感神经激活，这些变化将与PLP降低相关。 这一贡献是重要的，因为尽管有几项研究已经测试了tDCS对慢性炎症的疗效， 疼痛，有必要评估其在现实世界中的有效性，该提案提供了关键数据 开发一种安全和独特的干预措施，在家里应用，因此可以增加其获得 低代表性人群，降低治疗成本。这项研究还将提供机械 关于与该干预相关的副交感神经张力的响应和变化的预测因子的数据。的 拟议的研究是创新的，因为它为PLP治疗提供了一个务实的试验设计，旨在验证 一种基于家庭的tDCS设备，该设备是可行的，并且能够远程提供更长的治疗。这一建议也是 研究HRV作为降低PLP的替代标志物，并评估其在现实生活中的可行性。 最后，该提案使用机器学习算法验证对PLP治疗的反应的预测因子。